{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 聚类\n",
    "\n",
    "熟悉各中聚类算法的调用\n",
    "并用评价指标选择合适的超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入必要的工具包\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "import time\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取训练数据\n",
    "train = pd.read_csv('./UsedEvents.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<p>10 rows × 110 columns</p>\n",
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      ],
      "text/plain": [
       "     event_id     user_id                start_time city state  zip country  \\\n",
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       "1   244999119  3476440521  2012-11-03T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
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       "4  1051165850  1016098580  2012-09-27T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
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       "7  2584113432   613687941  2012-10-31T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
       "8  3365728297  1098509207  2012-10-31T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
       "9  2912638473  3598071768  2012-10-18T00:00:00.001Z  NaN   NaN  NaN     NaN   \n",
       "\n",
       "      lat     lng  c_1   ...     c_92  c_93  c_94  c_95  c_96  c_97  c_98  \\\n",
       "0     NaN     NaN    2   ...        0     1     0     0     0     0     0   \n",
       "1     NaN     NaN    2   ...        0     0     0     0     0     0     0   \n",
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       "3     NaN     NaN    1   ...        0     0     0     0     0     0     0   \n",
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       "6     NaN     NaN    0   ...        0     0     0     0     0     0     0   \n",
       "7     NaN     NaN    0   ...        2     0     0     0     0     0     0   \n",
       "8  47.058  21.926    0   ...        0     0     0     0     0     0     0   \n",
       "9     NaN     NaN    1   ...        0     0     0     0     0     0     0   \n",
       "\n",
       "   c_99  c_100  c_other  \n",
       "0     0      0        9  \n",
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       "2     0      0       12  \n",
       "3     0      0        8  \n",
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       "7     0      0      354  \n",
       "8     1      0       25  \n",
       "9     0      0        3  \n",
       "\n",
       "[10 rows x 110 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   c_1  c_2  c_3  c_4  c_5  c_6  c_7  c_8  c_9  c_10   ...     c_92  c_93  \\\n",
       "0    2    0    2    0    0    0    0    0    0     0   ...        0     1   \n",
       "1    2    0    2    0    0    0    0    0    0     0   ...        0     0   \n",
       "2    0    0    0    0    0    0    0    0    0     0   ...        0     0   \n",
       "3    1    0    2    1    0    0    0    0    0     0   ...        0     0   \n",
       "4    1    1    0    0    0    0    0    2    0     0   ...        0     0   \n",
       "5    0    0    0    0    0    0    0    0    0     0   ...        0     0   \n",
       "6    0    0    0    1    0    0    0    0    0     0   ...        0     0   \n",
       "7    0    0    2    0    0   33    0    3    1     0   ...        2     0   \n",
       "8    0    0    0    0    0    0    0    0    0     0   ...        0     0   \n",
       "9    1    0    1    0    1    0    0    0    0     0   ...        0     0   \n",
       "\n",
       "   c_94  c_95  c_96  c_97  c_98  c_99  c_100  c_other  \n",
       "0     0     0     0     0     0     0      0        9  \n",
       "1     0     0     0     0     0     0      0        7  \n",
       "2     0     0     0     0     0     0      0       12  \n",
       "3     0     0     0     0     0     0      0        8  \n",
       "4     0     0     0     0     0     0      0        9  \n",
       "5     0     0     0     0     0     0      0       22  \n",
       "6     0     0     0     0     0     0      0       28  \n",
       "7     0     0     0     0     0     0      0      354  \n",
       "8     0     0     0     0     0     1      0       25  \n",
       "9     0     0     0     0     0     0      0        3  \n",
       "\n",
       "[10 rows x 101 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DropCol=['event_id','user_id','start_time','city','state','zip','country','lat','lng']\n",
    "X_train=train.drop(DropCol,axis=1)\n",
    "X_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the shape of train_image: (13418, 101)\n"
     ]
    }
   ],
   "source": [
    "# 原始输入的特征维数和样本数目\n",
    "print('the shape of train_image: {}'.format(X_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一个参数点（聚类数据为K）的模型，在校验集上评价聚类算法性能\n",
    "def K_cluster_analysis_MiniBatch(K, X_train):\n",
    "    start = time.time()\n",
    "    \n",
    "    print(\"K-means MiniBatch begin with clusters: {}\".format(K));\n",
    "    \n",
    "    #K-means,在训练集上训练\n",
    "    mb_kmeans = MiniBatchKMeans(n_clusters = K)\n",
    "    mb_kmeans.fit(X_train)\n",
    "    \n",
    "    # 在训练集上测试\n",
    "    y_train_pred = mb_kmeans.predict(X_train)\n",
    "    \n",
    "\n",
    "    # K值的评估标准\n",
    "    #常见的方法有轮廓系数Silhouette Coefficient和Calinski-Harabasz Index\n",
    "    #这两个分数值越大则聚类效果越好\n",
    "    CH_score = metrics.calinski_harabaz_score(X_train,y_train_pred)\n",
    "    SI_score = metrics.silhouette_score(X_train,y_train_pred)\n",
    "    \n",
    "    end = time.time()\n",
    "    print(\"CH_score: {}, time elaps:{}\".format(CH_score, int(end-start)))\n",
    "    print(\"SI_score: {}\".format(SI_score))\n",
    "    \n",
    "    return CH_score,SI_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-means MiniBatch begin with clusters: 10\n",
      "CH_score: 1026.11114231, time elaps:4\n",
      "SI_score: 0.408042740944\n",
      "K-means MiniBatch begin with clusters: 20\n",
      "CH_score: 549.156643878, time elaps:3\n",
      "SI_score: 0.323109408559\n",
      "K-means MiniBatch begin with clusters: 30\n",
      "CH_score: 312.177984752, time elaps:3\n",
      "SI_score: 0.177693362131\n",
      "K-means MiniBatch begin with clusters: 40\n",
      "CH_score: 268.58973228, time elaps:3\n",
      "SI_score: 0.160300030139\n",
      "K-means MiniBatch begin with clusters: 50\n",
      "CH_score: 218.710433943, time elaps:3\n",
      "SI_score: 0.144342667296\n",
      "K-means MiniBatch begin with clusters: 60\n",
      "CH_score: 206.840610136, time elaps:3\n",
      "SI_score: 0.108190162933\n",
      "K-means MiniBatch begin with clusters: 70\n",
      "CH_score: 190.853921312, time elaps:3\n",
      "SI_score: 0.115160196192\n",
      "K-means MiniBatch begin with clusters: 80\n",
      "CH_score: 159.088346902, time elaps:4\n",
      "SI_score: 0.0967159269809\n",
      "K-means MiniBatch begin with clusters: 90\n",
      "CH_score: 398.569539139, time elaps:4\n",
      "SI_score: 0.0930174653048\n",
      "K-means MiniBatch begin with clusters: 100\n",
      "CH_score: 140.387420035, time elaps:4\n",
      "SI_score: 0.0926732673625\n"
     ]
    }
   ],
   "source": [
    "# 设置超参数（聚类数目K）搜索范围\n",
    "Ks_Mini = [10,20, 30,40,50,60,70,80,90,100]\n",
    "CH_scores_Mini = []\n",
    "SI_scores_Mini = []\n",
    "for K in Ks_Mini:\n",
    "    ch,si = K_cluster_analysis_MiniBatch(K, X_train)\n",
    "    CH_scores_Mini.append(ch)\n",
    "    SI_scores_Mini.append(si)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5aded3b710>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ks_Mini, np.array(CH_scores_Mini), 'b-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5adec4fb50>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ks_Mini, np.array(SI_scores_Mini), 'g-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里发现每次运行得到的最优K都不一样，在计算资源允许的情况下运行KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 7200x7200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示聚类结果\n",
    "#画出聚类结果，每一类用一种颜色\n",
    "colors = ['b','g','r','k','c','m','y','#e24fff','#524C90','#845868']\n",
    "\n",
    "n_clusters = 10\n",
    "mb_kmeans = MiniBatchKMeans(n_clusters = n_clusters)\n",
    "mb_kmeans.fit(X_train)\n",
    "\n",
    "y_train = mb_kmeans.labels_\n",
    "cents = mb_kmeans.cluster_centers_#质心\n",
    "\n",
    "x_axis = range(X_train.shape[1])\n",
    "\n",
    "plt.figure(figsize=(100,100)) \n",
    "for i in range(n_clusters):\n",
    "    index = np.nonzero(y_train==i)[0]\n",
    "    xs_i = X_train[index]\n",
    "    for j in range(min(len(xs_i),1000)):\n",
    "        plt.plot(np.array(x_axis), xs_i[j], colors[i])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一个参数点（聚类数据为K）的模型，在校验集上评价聚类算法性能\n",
    "def K_cluster_analysis_KMeans(K, X_train):\n",
    "    start = time.time()\n",
    "    \n",
    "    print(\"K-means begin with clusters: {}\".format(K));\n",
    "    \n",
    "    #K-means,在训练集上训练\n",
    "#    mb_kmeans = MiniBatchKMeans(n_clusters = K)\n",
    "#    mb_kmeans.fit(X_train)\n",
    "    kmeans = KMeans(n_clusters = K)\n",
    "    kmeans.fit(X_train)    \n",
    "    # 在训练集上测试\n",
    "    y_train_pred = kmeans.predict(X_train)\n",
    "    \n",
    "\n",
    "    # K值的评估标准\n",
    "    #常见的方法有轮廓系数Silhouette Coefficient和Calinski-Harabasz Index\n",
    "    #这两个分数值越大则聚类效果越好\n",
    "    CH_score = metrics.calinski_harabaz_score(X_train,y_train_pred)\n",
    "    SI_score = metrics.silhouette_score(X_train,y_train_pred)\n",
    "    \n",
    "    end = time.time()\n",
    "    print(\"CH_score: {}, time elaps:{}\".format(CH_score, int(end-start)))\n",
    "    print(\"SI_score: {}\".format(SI_score))\n",
    "    \n",
    "    return CH_score,SI_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-means begin with clusters: 10\n",
      "CH_score: 58824.77313, time elaps:5\n",
      "SI_score: 0.506671623344\n",
      "K-means begin with clusters: 20\n",
      "CH_score: 63849.0105407, time elaps:5\n",
      "SI_score: 0.362543086351\n",
      "K-means begin with clusters: 30\n",
      "CH_score: 54966.3544883, time elaps:6\n",
      "SI_score: 0.328778760065\n",
      "K-means begin with clusters: 40\n",
      "CH_score: 48873.7103903, time elaps:6\n",
      "SI_score: 0.270026726433\n",
      "K-means begin with clusters: 50\n",
      "CH_score: 44644.2477548, time elaps:7\n",
      "SI_score: 0.21048066342\n",
      "K-means begin with clusters: 60\n",
      "CH_score: 40525.3393355, time elaps:8\n",
      "SI_score: 0.205103200903\n",
      "K-means begin with clusters: 70\n",
      "CH_score: 37493.8965887, time elaps:7\n",
      "SI_score: 0.203450511549\n",
      "K-means begin with clusters: 80\n",
      "CH_score: 34726.5073553, time elaps:9\n",
      "SI_score: 0.171550444722\n",
      "K-means begin with clusters: 90\n",
      "CH_score: 32021.0782458, time elaps:9\n",
      "SI_score: 0.160503198631\n",
      "K-means begin with clusters: 100\n",
      "CH_score: 30102.7668371, time elaps:9\n",
      "SI_score: 0.160070526099\n"
     ]
    }
   ],
   "source": [
    "# 设置超参数（聚类数目K）搜索范围\n",
    "#Ks = [10,20, 30,40,50,60,70,80,90,100]\n",
    "Ks = [10,20, 30,40,50,60,70,80,90,100]\n",
    "CH_scores = []\n",
    "SI_scores = []\n",
    "for K in Ks:\n",
    "    ch,si = K_cluster_analysis_KMeans(K, X_train)\n",
    "    CH_scores.append(ch)\n",
    "    SI_scores.append(si)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5aca731350>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GpI92907uXhBfjwBmuns+MDO+BugH5MfHUGAshGQCjAIOB7oBo0oTSuwzNGW/vpV+R1JrlJYbPfXUUG503LikIxKRdFVltlJ/YEJ8PgEYkNL+qAezgSZm1gLoA8xw93Xuvh6YAfSN2xq7+yx3d+DRlGNJHZdabnToUJg4MemIRCQd6SYHB14ws/lmNjS27enuqwHizz1ie0tgZcq+xbFtW+3F5bSXYWZDzazQzApLSkrSDF2Sllpu9Be/ULlRkbog3eTQw907Ey4ZDTOzI7fRt7zxAq9Ee9lG9wfdvcDdC/Ly8iqKWWqRLcuNzpyZdEQisi1pJQd3XxV/rgH+Rhgz+CReEiL+XBO7FwN7p+zeClhVQXurctoly6SWG+3fX+VGRWqzCpODme1iZj8ofQ70BhYDU4HSGUeDgSnx+VTgrDhrqTuwIV52mg70NrOmcSC6NzA9bvvMzLrHWUpnpRxLskxqudF+/WDBgqQjEpHypHOfw57A3+Ls0gbAX9z9eTObBzxlZkOAj4DTYv9pwPFAEfAlcA6Au68zs5uAebHfje6+Lj6/AHgE2Al4Lj4kS5WWG/3xj0O50X/+Ew44IOmoRCSVhQlCdU9BQYEXFhYmHYZUwdKlIUE0aACvvgpt2yYdkUj2M7P5KbckbJUW3pPE5OeHS0yl5UYXLUo6IhEppeQgiTrkEJg+Hb76Crp2DQv11dGTWZGsouQgievaFd58E44+Gi68MNSGWL8+6ahEcpuSg9QKe+wBf/97WKhv6lTo1Aleey3pqERyl5KD1Br16sEVV8Drr4dB6p494be/hU2bko5MJPcoOUit07VruP/hpz+F3/wmTHddpdsiRWqUkoPUSo0bw+OPw/jxMHs2HHpouLtaRGqGkoPUWmahFnVhIfzwh3D88eGy08aNSUcmkv2UHKTWO+AAmDMHhg2DO++EHj2gqCjpqESym5KD1AmNGsG998LkybBsGXTuHOpEiEj1UHKQOuWUU2DhQujYEc48E849F774IumoRLKPkoPUOa1bwyuvwLXXwiOPQJcuIWGISOYoOUid1KAB3HhjKBr02WfQvXu47KSlN0QyQ8lB6rSjjw5nDcccAxdfHC47rVtX8X4ism1KDlLn5eXBs8+GmUzTpoV7Il59NemoROo2JQfJCmZw2WWh9GijRnDUUXDTTVp6Q6SylBwkq3TpAm+8AWecAdddB8ceCx9/nHRUInWPkoNknR/8AB57LMxkmjcvXGZ69tmkoxKpW5QcJCuZweDBMH8+7L03nHQS/OpX8PXXSUcmUjcoOUhW22+/MA5xySVw991wxBGhdrWIbFvaycHM6pvZAjN7Nr5+xMw+MLOF8dEptpuZ3WNmRWb2lpl1TjnGYDNbGh+DU9q7mNmiuM89ZmaZfJOS2xo1ColhyhT48MOw9MZjjyUdlUjttj1nDpcC72zRdpW7d4qP0ntU+wH58TEUGAtgZs2AUcDhQDdglJk1jfuMjX1L9+tbifcisk0nnxzKkXbuDGedFS47ff550lGJ1E5pJQczawWcAIxLo3t/4FEPZgNNzKwF0AeY4e7r3H09MAPoG7c1dvdZ7u7Ao8CAyrwZkYq0agUvvQTXXw9//nNIFG+8kXRUIrVPumcOdwHDge+2aP9tvHQ0xsx2jG0tgZUpfYpj27bai8tpF6kW9evDqFEhSXz5ZRiHuPtuLb0hkqrC5GBmJwJr3H3+FptGAvsDXYFmwNWlu5RzGK9Ee3mxDDWzQjMrLCkpqSh0kW3q2TNcZurTJ8xk6t8f1q5NOiqR2iGdM4cewMlmtgKYCPQysz+7++p46ehr4GHCOAKEv/z3Ttm/FbCqgvZW5bSX4e4PunuBuxfk5eWlEbrItjVvHgaq774bpk8P90TMnJl0VCLJqzA5uPtId2/l7m2AQcBL7v7zOFZAnFk0AFgcd5kKnBVnLXUHNrj7amA60NvMmsaB6N7A9LjtMzPrHo91FjAlw+9TZKvMwlTX2bND7erjjoPhw1WOVHJbVe5zeNzMFgGLgN2Bm2P7NGA5UAT8CbgQwN3XATcB8+LjxtgGcAFhsLsIWAaolLzUuMMOCzfNDR0Kt98exiLefTfpqESSYV5HR+EKCgq8sLAw6TAkS02ZAkOGhAHru+6CX/4ynGGI1HVmNt/dCyrqpzukRcrRvz+89Rb06AHnnQenngr//nfSUYnUHCUHka344Q/DIPXo0WHhvo4d4cUXk45KpGYoOYhsQ716cMUVMHfu5sHqq67SAn6S/ZQcRNLQqVMYrL7ggnAmocFqyXZKDiJp2nlnuP/+MFi9cmVYeuOPf9Sd1ZKdlBxEttPJJ4fB6v/5Hzj/fDjlFN1ZLdlHyUGkElq0gOefhzvvhOeeC4PVM2YkHZVI5ig5iFRSvXpw2WUwZw40aQK9e4fBaw1WSzZQchCpok6doLAQLrwwnEl07w7vbFn5RKSOUXIQyYCdd4b77oNnnoHi4jBYPXasBqul7lJyEMmgE0+ERYvCcuAXXggDBoBWl5e6SMlBJMP22gumTYMxY8KgdceO8MILSUclsn2UHESqQb16oYDQvHnQrFkoKHT55RqslrpDyUGkGnXsGAarL7oonEkcfji8/XbSUYlUTMlBpJrttBP84Q9h8b5Vq6BLl3CntQarpTZTchCpISecEO6sPuooGDYsLAuuwWqprZQcRGrQXnvB3/8eala/8AIcckhYFlyktlFyEKlh9eqFmtXz5sHuu0PfvuFO6//+N+nIRDZTchBJyCGHhARx8cWhFOnhh8OSJUlHJRIoOYgkaKed4J57wqWmf/0LCgrCndYarJakpZ0czKy+mS0ws2fj67ZmNsfMlprZk2bWMLbvGF8Xxe1tUo4xMra/Z2Z9Utr7xrYiMxuRubcnUjccf3wYrO7VK0x7PfbYMAVWJCnbc+ZwKZC6nNitwBh3zwfWA0Ni+xBgvbt3AMbEfpjZgcAg4CCgL3B/TDj1gfuAfsCBwBmxr0hO2XPPMN31/vtDoujaFU49VYv4STLSSg5m1go4ARgXXxvQC5gUu0wABsTn/eNr4vZjYv/+wER3/9rdPwCKgG7xUeTuy919IzAx9hXJOWahFOmyZXDDDaFGxMEHw9lnw4oVSUcnuSTdM4e7gOHAd/F1c+BTd/82vi4GWsbnLYGVAHH7htj//7dvsc/W2kVyVuPGcN11sHx5WHbjySdh333D4PW//pV0dJILKkwOZnYisMbd56c2l9PVK9i2ve3lxTLUzArNrLBEdw9JDth9d7j9digqgnPPDcuAt28P11wD69cnHZ1ks3TOHHoAJ5vZCsIln16EM4kmZtYg9mkFrIrPi4G9AeL23YB1qe1b7LO19jLc/UF3L3D3gry8vDRCF8kOLVvCAw/Au++GZcBvuQXatYPf/x6++CLp6CQbVZgc3H2ku7dy9zaEAeWX3P1M4GVgYOw2GJgSn0+Nr4nbX3J3j+2D4mymtkA+MBeYB+TH2U8N4++YmpF3J5JlOnSAxx+HhQvhxz8OZxDt28O992rFV8msqtzncDVwuZkVEcYUHortDwHNY/vlwAgAd18CPAW8DTwPDHP3TXFc4iJgOmE21FOxr4hsRceOMHUqvP46HHBAGIvYbz945BHYtCnp6CQbmNfRu20KCgq8UBPBRXCHF18MZxGFhSFZ3HQT/OQnYfaTSCozm+/uBRX10x3SInWcGRx3HMydC3/9a2gbODDcJ/HCC7rbWipHyUEkS5iFs4VFi8LlpbVrQwW6o4+GWbOSjk7qGiUHkSxTvz4MHgzvvReKDL37LvzoR3DyyeHOa5F0KDmIZKkddwzrNC1bFqa8vvoqdOoEP/tZuG9CZFuUHESy3C67wIgR4W7rkSNhyhTYf3847zz4+OOko5PaSslBJEc0bQq//W04k7jwQnj44XCPxJVXhvEJkVRKDiI5Zq+9Qg2J99+HM86AMWPC3dY33AD/+U/S0UltoeQgkqPatAlnD4sXQ+/ecP31IUnceSd89VXS0UnSlBxEctwBB8CkSaFkaZcucMUVkJ8PDz4I33yTdHSSFCUHEQFCidLp0+Hll6F16zBgve++YSXY//436eikpik5iMj3HHUUvPZaqEq3115h8LpdO7jjDvj886Sjk5qi5CAiZZjBCSeEhf1mzoQDDwyzmvbZB268UbUkcoGSg4hslRn06hUW9ps1C3r0gFGjwmWnq6+GTz5JOkKpLkoOIpKW7t3DMuFvvgknngijR4cZTxdfDB99lHR0kmlKDiKyXTp2hCeeCGs2/exnoUJd+/ahjOn77ycdnWSKkoOIVEp+Pjz0ULjj+oILQsI44AAYNEgL/GUDJQcRqZLWrcMd1ytWwFVXwbRpcOihcNJJMHt20tFJZSk5iEhG7Lkn3HILfPhhmNH0+utwxBFwzDFhxpOKDtUtSg4iklFNm8K114Ykcccd8M47cOyxIVE884ySRF2h5CAi1WLXXeHyy8NS4WPHhmmvJ58cakpMnAibNiUdoWyLkoOIVKtGjeD888NMpkcfDes1nXFGGLwePx42bkw6QilPhcnBzBqZ2Vwze9PMlpjZDbH9ETP7wMwWxken2G5mdo+ZFZnZW2bWOeVYg81saXwMTmnvYmaL4j73mJlVx5sVkeTssAP84hdhFdhJk8KZxZAh0KFDKGeqlWBrl3TOHL4Gern7oUAnoK+ZdY/brnL3TvGxMLb1A/LjYygwFsDMmgGjgMOBbsAoM2sa9xkb+5bu17fK70xEaqV69eDUU2H+fHjuubAkxyWXhBvqbr1VNSVqiwqTgwely23tEB/bGlLqDzwa95sNNDGzFkAfYIa7r3P39cAMQqJpATR291nu7sCjwIAqvCcRqQPMoG/fUNv6H/+Aww4L5Uz32Qeuuw7+/e+kI8xtaY05mFl9M1sIrCH8Bz8nbvptvHQ0xsx2jG0tgZUpuxfHtm21F5fTXl4cQ82s0MwKS0pK0gldROqAI4+E558PNSWOPhpuuikkiSuvhFWrko4uN6WVHNx9k7t3AloB3czsYGAksD/QFWgGXB27lzde4JVoLy+OB929wN0L8vLy0gldROqQggKYPDmMS5xyCtx1V1gu/Pzzoago6ehyy3bNVnL3T4FXgL7uvjpeOvoaeJgwjgDhL/+9U3ZrBayqoL1VOe0ikqMOOggeeyzMcDr77FDOdL/9wtIcCxYkHV1uSGe2Up6ZNYnPdwKOBd6NYwXEmUUDgMVxl6nAWXHWUndgg7uvBqYDvc2saRyI7g1Mj9s+M7Pu8VhnAVMy+zZFpC5q1y4s7Fe6NMdzz0HnzmGs4pVXdENddUrnzKEF8LKZvQXMI4w5PAs8bmaLgEXA7sDNsf80YDlQBPwJuBDA3dcBN8VjzANujG0AFwDj4j7LgOeq/tZEJFu0aBGW5vjoI/j972HhwjA2ccQR8Le/wXffJR1h9jGvo6m3oKDACwsLkw5DRBLw1VcwYQLcfnu4A3v//WH4cDjzTGjYMOnoajczm+/uBRX10x3SIlLn7LRTGKR+772wVHijRqGeRPv2MGaMal1ngpKDiNRZDRqEQeo33ghTYTt0COs5tW4dypmuXZt0hHWXkoOI1Hlm0KcPvPxyqHXds2dYNrx1a7j00rBCrGwfJQcRySrdu4dB6iVL4PTT4f77wxnF4MGhTdKj5CAiWenAA8P9EcuXw0UXhcX+Dj44LBv++utJR1f7KTmISFbbe+8wSP3RR3D99fDaa9CjR1iyY9o03SuxNUoOIpITmjcPg9QffRSW5VixAk44IRQf+stf4Ntvk46wdlFyEJGcsssuYZC6qAgeeSQUHzrzTNh33zA+oboSgZKDiOSkhg3DIPXixfD007DHHjBsWFgN9ne/g08/TTrCZCk5iEhOq1cP+vcPU2BfeQW6dIFf/zpMgx0+PHeXDFdyEBEh3CvRs2dY3G/BAjjxRLjjDmjbFoYOhaVLk46wZik5iIhsoXSQ+v33Q53rRx8NYxI9eoTB7OLiio9R1yk5iIhsRfv2YZB6xQq4+Wb44gu47LIwPfZHPwpTZFeurPAwdZKSg4hIBfbaK4xDLFwYFvu7+Wb48svN6zgdcQTceWeYJpsttGS3iEglLV0K//d/4bFwYWg7/HA47TQYODDMfKpt0l2yW8n9tRKgAAAGeUlEQVRBRCQDioo2J4rSUqa1MVGonoOISA3q0AFGjgzLhy9dGirWffMNXHkltGkTEsXo0WH8oi5QchARybAOHWDECJg/P5xR3HILbNoU6mC3bQvduoUqdrU5USg5iIhUo/bt4eqrobAQli2DW28Ni/0NHx4SRdeucNtt8MEHSUf6fUoOIiI1pF27kBTmzducKCAkj3btoKAgtC1fnmyckEZyMLNGZjbXzN40syVmdkNsb2tmc8xsqZk9aWYNY/uO8XVR3N4m5VgjY/t7ZtYnpb1vbCsysxGZf5siIrVLaqJYvjycPdSrFy5HtW+ffKJI58zha6CXux8KdAL6mll34FZgjLvnA+uBIbH/EGC9u3cAxsR+mNmBwCDgIKAvcL+Z1Tez+sB9QD/gQOCM2FdEJCe0bRvGI+bODZeXbr8d6tffnCi6dAnjFsuW1VxMFSYHDz6PL3eIDwd6AZNi+wRgQHzeP74mbj/GzCy2T3T3r939A6AI6BYfRe6+3N03AhNjXxGRnNOmTZjhNGdOGLAePRp22CHMhOrQATp3hjVrqj+OtMYc4l/4C4E1wAxgGfCpu5eWxygGWsbnLYGVAHH7BqB5avsW+2ytXUQkp+2zD1xxBcyeDR9+GBYCbNMG8vKq/3enlRzcfZO7dwJaEf7SP6C8bvGnbWXb9raXYWZDzazQzApLSkoqDlxEJEu0bh2W65g8OawgW922a7aSu38KvAJ0B5qYWYO4qRVQuup5MbA3QNy+G7AutX2LfbbWXt7vf9DdC9y9IK8mUqeISI5KZ7ZSnpk1ic93Ao4F3gFeBgbGboOBKfH51PiauP0lD2t0TAUGxdlMbYF8YC4wD8iPs58aEgatp2bizYmISOU0qLgLLYAJcVZRPeApd3/WzN4GJprZzcAC4KHY/yHgMTMrIpwxDAJw9yVm9hTwNvAtMMzdNwGY2UXAdKA+MN7dl2TsHYqIyHbTwnsiIjlEC++JiEilKTmIiEgZSg4iIlKGkoOIiJRRZwekzawE+DDpOKpod2Bt0kHUEvosvk+fx/fp89isqp/FPu5e4Y1idTY5ZAMzK0xn1kAu0Gfxffo8vk+fx2Y19VnospKIiJSh5CAiImUoOSTrwaQDqEX0WXyfPo/v0+exWY18FhpzEBGRMnTmICIiZSg51AAz29vMXjazd2Id7ktjezMzmxHrcM8ws6ZJx1qTYhGpBWb2bHxdbl3ybGdmTcxskpm9G78jR+Tyd8PMLov/Thab2ROxjn3OfDfMbLyZrTGzxSlt5X4fLLjHzIrM7C0z65ypOJQcasa3wBXufgChFsawWCd7BDAz1uGeGV/nkksJy7+X2lpd8mx3N/C8u+8PHEr4THLyu2FmLYFLgAJ3P5iwUvMgcuu78QjQd4u2rX0f+hHKH+QDQ4GxmQpCyaEGuPtqd38jPv+M8I+/Jd+vt51ahzvrmVkr4ARgXHxtbL0uedYys8bAkcQl7919YyyqlbPfDUIpgZ1isbCdgdXk0HfD3f9JKHeQamvfh/7Aox7MJhRha5GJOJQcapiZtQEOA+YAe7r7aggJBNgjuchq3F3AcOC7+Lo5W69Lns3aASXAw/ES2zgz24Uc/W64+8fAaOAjQlLYAMwnN78bqbb2fWgJrEzpl7HPRsmhBpnZrsBfgV+5+3+SjicpZnYisMbd56c2l9M1F6bSNQA6A2Pd/TDgC3LkElJ54rX0/kBb4IfALoRLJ1vKhe9GOqrt342SQw0xsx0IieFxd58cmz8pPQWMP9ckFV8N6wGcbGYrgImESwZ3sfW65NmsGCh29znx9SRCssjV78axwAfuXuLu3wCTgR+Rm9+NVFv7PhQDe6f0y9hno+RQA+L19IeAd9z9zpRNqfW2U+twZzV3H+nurdy9DWGw8SV3P5Ot1yXPWu7+L2Clme0Xm44hlNLNye8G4XJSdzPbOf67Kf08cu67sYWtfR+mAmfFWUvdgQ2ll5+qSjfB1QAz+x/gVWARm6+xX0MYd3gKaE34R3Gau285EJXVzOwo4Ep3P9HM2hHOJJoR6pL/3N2/TjK+mmBmnQgD8w2B5cA5xHrt5OB3w8xuAE4nzPJbAPwv4Tp6Tnw3zOwJ4CjC6qufAKOApynn+xAT6L2E2U1fAue4e0bqJys5iIhIGbqsJCIiZSg5iIhIGUoOIiJShpKDiIiUoeQgIiJlKDmIiEgZSg4iIlKGkoOIiJTx/wChs8ba3boTywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ks, np.array(CH_scores), 'b-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ad6374790>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ks, np.array(SI_scores), 'g-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的SI得分在K=10的时候，大于0.5表明此时聚类合适。这里再调整看看。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-means begin with clusters: 6\n",
      "CH_score: 41566.1702934, time elaps:5\n",
      "SI_score: 0.579035510353\n",
      "K-means begin with clusters: 7\n",
      "CH_score: 47745.8999606, time elaps:5\n",
      "SI_score: 0.542028980466\n",
      "K-means begin with clusters: 8\n",
      "CH_score: 52259.4363526, time elaps:5\n",
      "SI_score: 0.523192268697\n",
      "K-means begin with clusters: 9\n",
      "CH_score: 55394.3726717, time elaps:5\n",
      "SI_score: 0.498143736113\n",
      "K-means begin with clusters: 10\n",
      "CH_score: 58819.7475209, time elaps:5\n",
      "SI_score: 0.503584027894\n",
      "K-means begin with clusters: 11\n",
      "CH_score: 63268.7088197, time elaps:5\n",
      "SI_score: 0.48360604398\n",
      "K-means begin with clusters: 12\n",
      "CH_score: 67398.9558978, time elaps:5\n",
      "SI_score: 0.474480322137\n",
      "K-means begin with clusters: 13\n",
      "CH_score: 68562.4088811, time elaps:5\n",
      "SI_score: 0.447217460289\n",
      "K-means begin with clusters: 14\n",
      "CH_score: 69042.7535189, time elaps:5\n",
      "SI_score: 0.443046534095\n",
      "K-means begin with clusters: 15\n",
      "CH_score: 69223.8768538, time elaps:5\n",
      "SI_score: 0.416320205227\n"
     ]
    }
   ],
   "source": [
    "# 重新设置超参数（聚类数目K）搜索范围\n",
    "Ks_Min = [6,7, 8,9,10,11,12,13,14,15]\n",
    "CH_scores = []\n",
    "SI_scores = []\n",
    "for K in Ks_Min:\n",
    "    ch,si = K_cluster_analysis_KMeans(K, X_train)\n",
    "    CH_scores.append(ch)\n",
    "    SI_scores.append(si)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ad6366c10>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ks_Min, np.array(SI_scores), 'g-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ad62e4590>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ks_Min, np.array(CH_scores), 'b-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-means begin with clusters: 2\n",
      "CH_score: 19510.1717318, time elaps:4\n",
      "SI_score: 0.993903490063\n",
      "K-means begin with clusters: 3\n",
      "CH_score: 24508.4406842, time elaps:4\n",
      "SI_score: 0.736598334223\n",
      "K-means begin with clusters: 4\n",
      "CH_score: 29747.5248777, time elaps:5\n",
      "SI_score: 0.62631668377\n",
      "K-means begin with clusters: 5\n",
      "CH_score: 34098.8905375, time elaps:5\n",
      "SI_score: 0.58345531221\n",
      "K-means begin with clusters: 6\n",
      "CH_score: 41566.1702934, time elaps:5\n",
      "SI_score: 0.579035510353\n"
     ]
    }
   ],
   "source": [
    "# 重新设置超参数（聚类数目K）搜索范围\n",
    "Ks_Min = [2, 3,4,5,6]\n",
    "CH_scores = []\n",
    "SI_scores = []\n",
    "for K in Ks_Min:\n",
    "    ch,si = K_cluster_analysis_KMeans(K, X_train)\n",
    "    CH_scores.append(ch)\n",
    "    SI_scores.append(si)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ad625a050>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ks_Min, np.array(SI_scores), 'g-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ad61c9950>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ks_Min, np.array(CH_scores), 'b-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可能由于两种评价方法的计算公式不一样，加上样本方差不一样，导致在类较少的时候分出的结果不一样。这里可以选择SI分数大于0.5时，CH分数最大的K值。也就是K=10\n",
    "由于这里最后一个特征的取值跟其他差别较大，这里对数据正则化试试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
